Outsmarting traffic? A traffic complexity simulation of the effect of traffic information apps on traffic congestion - Azcarraga, Arno Mikhail J. (Animo Repository, 2020)
As traffic congestion continues to be one of the global urban problems due to transportation supply and demand disequilibrium, current solutions to address traffic through the development of traffic information apps (TIAs) might be counter-intuitive as TIAs are designed as a mechanism to decongest traffic in roads, but economic theories such as the informational Braess’ paradox suggest that they worsen traffic even more. This paper observes the presence of the informational Braess’ paradox in a real road network simulation, verify the existence of Bayesian and experiential learning among a network of drivers, and identify the optimal proportion of TIA users to non-users in the short run and the long run. Although results have also shown that the IBP is not present in the short run, the long run results show the presence of the IBP, which is claimed to be caused by the presence of learning-by-doing. This simulation also shows that there are multiple equilibria in the proportion of TIA users to non-users. Moreover, the results also confirm the presence of Bayesian learning and experiential learning in the usage of TIAs, making them more efficient with more drivers in the system. Policy insights are presented to contribute to the solution of traffic congestion, which if left unchecked would continue to stall productivity gains despite technological progress that the use of TIAs represents.